[Coursera] Practical Data Science on the AWS Cloud Specialization

By Course Club Last updated Jul 12, 2022

Become a cloud data science expert. Develop and scale your data science projects into the cloud using Amazon SageMaker

What you will learn

Prepare data, detect statistical data biases, perform feature engineering at scale to train models, & train, evaluate, & tune models with AutoML
Store & manage ML features using a feature store, & debug, profile, tune, & evaluate models while tracking data lineage and model artifacts
Build, deploy, monitor, & operationalize end-to-end machine learning pipelines

Build data labeling and human-in-the-loop pipelines to improve model performance with human intelligence

Description

Practical Data Science Specialization is an applied science training package published by the Coursera Academy. These two trainings are organized by DeepLearning.AI and mazon Web Services foundations, and during the training process, you will get acquainted with the process of developing, scaling, and implementing data science projects on the Amazon SageMaker cloud server platform. Development environments are very different from the final product production and implementation environment, and require fewer prerequisites and considerations to develop decades. The transfer of data-driven and machine-based projects from the conceptualization and design stage to the production of the final product requires scattered sets of skills that not every developer possesses. The overall architecture and structure of your project should be such that you provide the best performance with the least resources and the process of development and use is easy.

Science is a vast interdisciplinary industry that requires skills in mathematics, statistics, statistics, and programming. This training package is completely dedicated to developers, analysts and knowledge that is designed to deal with data on a daily basis and its applicants are expected to be designed in the programming language of Python, SQL and systems . Master database management.

What you will learn in Practical Data Science Specialization

Initial data collection and preparation
Detection of biases and defects of raw statistical data
Practice, evaluate and optimize different models using AutoML
Design, implement, monitor and manage machine learning pipeline operations
Natural language processing with the BERT library
A / B testing of different machine learning models
Automatic machine learning
Multiple classification with FastText and BlazingText libraries
Forbidden data
Exploratory data analysis
Evaluate and troubleshoot different machine learning models

Course Specifications

Publisher: Coursera
Instructors: Antje Barth, Shelbee Eigenbrode, Sireesha Muppala and Chris Fregly
Language: English
Level: Advanced
institution/university: DeepLearning.AI and Amazon Web Services
Number of Courses: 3
Duration: Approximately 3 months to complete – Suggested pace of 5 hours/week

Size: 561.05 MB

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https://www.coursera.org/specializations/practical-data-science.